store = {}
store['args']={'name': 'bald_mnist_755767', 'type': 'AcquisitionFunction.bald', 'seed': 755767, 'experiment_description': 'Coreset BALD vs BALD', 'acquisition_method': 'AcquisitionMethod.independent', 'available_sample_k': 1, 'num_inference_samples': 20, 'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 1024, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'target_accuracy': 0.96, 'target_num_acquired_samples': 300, 'log_interval': 20, 'dataset': 'DatasetEnum.mnist', 'initial_samples': [38043, 40091, 17418, 2094, 39879, 3133, 5011, 40683, 54379, 24287, 9849, 59305, 39508, 39356, 8758, 52579, 13655, 7636, 21562, 41329], 'experiment_task_id': 7, 'experiments_laaos': './experiment_configs/coreset_bald_vs_bald/configs.py', 'no_cuda': False, 'quickquick': False, 'initial_samples_per_class': 2}
store['cmdline']=['./src/ignite_mnist.py', '--experiment_task_id=7', '--experiments_laaos=./experiment_configs/coreset_bald_vs_bald/configs.py']
store['iterations']=[]
store['initial_samples']=[38043, 40091, 17418, 2094, 39879, 3133, 5011, 40683, 54379, 24287, 9849, 59305, 39508, 39356, 8758, 52579, 13655, 7636, 21562, 41329]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6358, 'nll': 2.691256618118286}, 'chosen_samples': ['18324'], 'chosen_samples_score': [1.2378782396698327], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.5865, 'nll': 2.8562402740478516}, 'chosen_samples': ['38171'], 'chosen_samples_score': [1.2311247715030191], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6347, 'nll': 2.5038830726623535}, 'chosen_samples': ['40562'], 'chosen_samples_score': [1.212371049542169], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6759, 'nll': 2.2692558753967287}, 'chosen_samples': ['56932'], 'chosen_samples_score': [1.1904893687865765], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6589, 'nll': 2.3997261711120608}, 'chosen_samples': ['33200'], 'chosen_samples_score': [1.216472463560486], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6943, 'nll': 1.9140941150665283}, 'chosen_samples': ['30322'], 'chosen_samples_score': [1.1485413030809308], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7166, 'nll': 1.6476776231765746}, 'chosen_samples': ['28853'], 'chosen_samples_score': [1.1477454257295951], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7088, 'nll': 1.698189208984375}, 'chosen_samples': ['28310'], 'chosen_samples_score': [1.2565421656098497], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7123, 'nll': 1.6215572868347168}, 'chosen_samples': ['15372'], 'chosen_samples_score': [1.229427679517232], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7039, 'nll': 1.7339098146438598}, 'chosen_samples': ['2743'], 'chosen_samples_score': [1.1223873710207593], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6965, 'nll': 1.6930306732177733}, 'chosen_samples': ['57212'], 'chosen_samples_score': [1.0955990935294935], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7344, 'nll': 1.5615054851531982}, 'chosen_samples': ['53083'], 'chosen_samples_score': [1.1526744977395504], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7387, 'nll': 1.511166681289673}, 'chosen_samples': ['46789'], 'chosen_samples_score': [1.1521096974408636], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7511, 'nll': 1.419440005683899}, 'chosen_samples': ['47181'], 'chosen_samples_score': [1.102060589629082], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7556, 'nll': 1.3295991495132446}, 'chosen_samples': ['42723'], 'chosen_samples_score': [1.0462087244947444], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7853, 'nll': 1.2196093759536744}, 'chosen_samples': ['57632'], 'chosen_samples_score': [1.1385582921741761], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7603, 'nll': 1.332210482406616}, 'chosen_samples': ['11213'], 'chosen_samples_score': [1.1105448824994597], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8026, 'nll': 1.1762601490020752}, 'chosen_samples': ['52169'], 'chosen_samples_score': [1.2556300345953142], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7672, 'nll': 1.299932525062561}, 'chosen_samples': ['14656'], 'chosen_samples_score': [1.0617220779308685], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.774, 'nll': 1.2948330383300781}, 'chosen_samples': ['24038'], 'chosen_samples_score': [1.1208195789990345], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7431, 'nll': 1.3673732570648194}, 'chosen_samples': ['10916'], 'chosen_samples_score': [1.1197879300216216], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.746, 'nll': 1.393963811302185}, 'chosen_samples': ['12035'], 'chosen_samples_score': [1.0588582682853491], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.744, 'nll': 1.3441975507736206}, 'chosen_samples': ['23397'], 'chosen_samples_score': [1.0441254560649185], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7667, 'nll': 1.2455219877243042}, 'chosen_samples': ['29334'], 'chosen_samples_score': [1.088844150751542], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7694, 'nll': 1.2688725217819214}, 'chosen_samples': ['27313'], 'chosen_samples_score': [0.9872420954125363], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7771, 'nll': 1.184662607383728}, 'chosen_samples': ['4694'], 'chosen_samples_score': [1.0355679231480508], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7638, 'nll': 1.2675911653518677}, 'chosen_samples': ['33682'], 'chosen_samples_score': [0.9896470748078248], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7675, 'nll': 1.255749304008484}, 'chosen_samples': ['55875'], 'chosen_samples_score': [1.0134349052646527], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7944, 'nll': 1.0604707874298096}, 'chosen_samples': ['36072'], 'chosen_samples_score': [0.9630024754331697], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7733, 'nll': 1.177173020362854}, 'chosen_samples': ['13075'], 'chosen_samples_score': [1.012507457778608], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.786, 'nll': 1.1717927278518676}, 'chosen_samples': ['28179'], 'chosen_samples_score': [1.0790279218948677], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7763, 'nll': 1.1291775014877319}, 'chosen_samples': ['17131'], 'chosen_samples_score': [1.0991637731468047], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7778, 'nll': 1.1392717935562133}, 'chosen_samples': ['4446'], 'chosen_samples_score': [1.021541965205086], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7916, 'nll': 1.034984009361267}, 'chosen_samples': ['47132'], 'chosen_samples_score': [0.9557004593820232], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7754, 'nll': 1.124389326095581}, 'chosen_samples': ['8586'], 'chosen_samples_score': [0.9334286515448434], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7776, 'nll': 1.1177262859344483}, 'chosen_samples': ['23175'], 'chosen_samples_score': [0.9328635327363486], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7763, 'nll': 1.0632002584457398}, 'chosen_samples': ['17714'], 'chosen_samples_score': [0.9706911521015942], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8002, 'nll': 1.0124827703475952}, 'chosen_samples': ['57441'], 'chosen_samples_score': [0.9363984015501248], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.822, 'nll': 0.9366221895217895}, 'chosen_samples': ['11091'], 'chosen_samples_score': [0.9636365940646502], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8119, 'nll': 0.996487512588501}, 'chosen_samples': ['33773'], 'chosen_samples_score': [1.0629496655130684], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8089, 'nll': 0.960018505859375}, 'chosen_samples': ['14520'], 'chosen_samples_score': [0.9367203701835711], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8068, 'nll': 0.9670252311706543}, 'chosen_samples': ['14769'], 'chosen_samples_score': [0.9996799435900978], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8073, 'nll': 0.9771275199890137}, 'chosen_samples': ['52896'], 'chosen_samples_score': [1.025595272570607], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8248, 'nll': 0.9207357345581054}, 'chosen_samples': ['28469'], 'chosen_samples_score': [0.9975930445233141], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8351, 'nll': 0.8798962923049927}, 'chosen_samples': ['14394'], 'chosen_samples_score': [1.0660847101258673], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8571, 'nll': 0.8627544744491578}, 'chosen_samples': ['134'], 'chosen_samples_score': [1.2877793975683964], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8037, 'nll': 0.988823711013794}, 'chosen_samples': ['24426'], 'chosen_samples_score': [0.9699197243434023], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8016, 'nll': 0.9835032123565673}, 'chosen_samples': ['19719'], 'chosen_samples_score': [0.909462948852646], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8325, 'nll': 0.8818916223526001}, 'chosen_samples': ['37256'], 'chosen_samples_score': [0.9044746155102661], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.849, 'nll': 0.8448539342880249}, 'chosen_samples': ['24558'], 'chosen_samples_score': [1.1001043520661282], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8333, 'nll': 0.8736484418869018}, 'chosen_samples': ['30400'], 'chosen_samples_score': [0.8909201197059418], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8538, 'nll': 0.8238025615692138}, 'chosen_samples': ['21395'], 'chosen_samples_score': [0.8838554287499917], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8242, 'nll': 0.9225078645706177}, 'chosen_samples': ['14403'], 'chosen_samples_score': [0.9055682957138905], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8213, 'nll': 0.9482157341003418}, 'chosen_samples': ['46615'], 'chosen_samples_score': [0.9377604510173029], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8112, 'nll': 0.9353101438522339}, 'chosen_samples': ['5137'], 'chosen_samples_score': [0.8967183279988133], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8556, 'nll': 0.8448715724945068}, 'chosen_samples': ['49354'], 'chosen_samples_score': [1.1223258741733997], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8474, 'nll': 0.8696704692840577}, 'chosen_samples': ['14043'], 'chosen_samples_score': [1.074975200710337], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8202, 'nll': 0.9143072059631348}, 'chosen_samples': ['24398'], 'chosen_samples_score': [0.7932266621141838], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8454, 'nll': 0.8793471590042115}, 'chosen_samples': ['46529'], 'chosen_samples_score': [1.1177063262524483], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8577, 'nll': 0.7852681365966797}, 'chosen_samples': ['59335'], 'chosen_samples_score': [1.0520117669465108], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8588, 'nll': 0.8523190412521362}, 'chosen_samples': ['23678'], 'chosen_samples_score': [1.0972581383675046], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8456, 'nll': 0.816998893737793}, 'chosen_samples': ['20903'], 'chosen_samples_score': [0.8480637068860175], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8565, 'nll': 0.8450762681961059}, 'chosen_samples': ['48370'], 'chosen_samples_score': [1.0790749243612943], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8481, 'nll': 0.8544853353500366}, 'chosen_samples': ['28313'], 'chosen_samples_score': [1.1003212786471508], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8283, 'nll': 0.8871224708557129}, 'chosen_samples': ['45800'], 'chosen_samples_score': [0.8280623975613238], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8168, 'nll': 0.925466276550293}, 'chosen_samples': ['12897'], 'chosen_samples_score': [0.8310887687737895], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8749, 'nll': 0.8041372396469116}, 'chosen_samples': ['45026'], 'chosen_samples_score': [1.2141882328460436], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8616, 'nll': 0.7861783439636231}, 'chosen_samples': ['44998'], 'chosen_samples_score': [1.018976940962813], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8594, 'nll': 0.8003119714736938}, 'chosen_samples': ['42415'], 'chosen_samples_score': [1.1005072768585022], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8558, 'nll': 0.8338990495681763}, 'chosen_samples': ['10995'], 'chosen_samples_score': [1.048189386731974], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8704, 'nll': 0.8258973606109619}, 'chosen_samples': ['28666'], 'chosen_samples_score': [1.1396465401019007], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8689, 'nll': 0.7347104528427124}, 'chosen_samples': ['10986'], 'chosen_samples_score': [1.088396409604952], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8665, 'nll': 0.7566222665786743}, 'chosen_samples': ['19638'], 'chosen_samples_score': [1.1245296353590968], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8761, 'nll': 0.7395037866592408}, 'chosen_samples': ['20476'], 'chosen_samples_score': [1.0937848952869635], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8575, 'nll': 0.8159333045959473}, 'chosen_samples': ['6272'], 'chosen_samples_score': [0.9782913103244819], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8627, 'nll': 0.7725570215225219}, 'chosen_samples': ['49509'], 'chosen_samples_score': [0.9900696635287307], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8706, 'nll': 0.7052436471939086}, 'chosen_samples': ['3719'], 'chosen_samples_score': [1.0535370917230393], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8601, 'nll': 0.8040091087341309}, 'chosen_samples': ['12349'], 'chosen_samples_score': [1.142226430690192], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8584, 'nll': 0.8343263973236084}, 'chosen_samples': ['24820'], 'chosen_samples_score': [1.0617226835822755], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8684, 'nll': 0.7562648147583008}, 'chosen_samples': ['10312'], 'chosen_samples_score': [1.0053412186492134], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.872, 'nll': 0.7289086912155152}, 'chosen_samples': ['1239'], 'chosen_samples_score': [1.0607363999074513], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8789, 'nll': 0.7311373903274536}, 'chosen_samples': ['10265'], 'chosen_samples_score': [1.0836756133557888], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8781, 'nll': 0.717907002067566}, 'chosen_samples': ['28658'], 'chosen_samples_score': [1.0576623253208761], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8759, 'nll': 0.7509237426757812}, 'chosen_samples': ['3494'], 'chosen_samples_score': [1.1033682689967121], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8758, 'nll': 0.705851050567627}, 'chosen_samples': ['17817'], 'chosen_samples_score': [0.9407010282538782], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8703, 'nll': 0.7162986342430114}, 'chosen_samples': ['14749'], 'chosen_samples_score': [1.0237631660782376], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8835, 'nll': 0.685880344581604}, 'chosen_samples': ['40702'], 'chosen_samples_score': [1.0609641160566707], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8685, 'nll': 0.7724641876220704}, 'chosen_samples': ['27716'], 'chosen_samples_score': [1.0502889764968062], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8679, 'nll': 0.7254384902954102}, 'chosen_samples': ['23190'], 'chosen_samples_score': [0.9577889143535265], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8877, 'nll': 0.6693042833328247}, 'chosen_samples': ['20172'], 'chosen_samples_score': [0.997950948573456], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8736, 'nll': 0.6911307750701904}, 'chosen_samples': ['33222'], 'chosen_samples_score': [1.0018913591793863], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.8909, 'nll': 0.734785757446289}, 'chosen_samples': ['17756'], 'chosen_samples_score': [1.214175155146254], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.8874, 'nll': 0.7254648286819458}, 'chosen_samples': ['42746'], 'chosen_samples_score': [1.2302966089148923], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8786, 'nll': 0.6938330991744995}, 'chosen_samples': ['13983'], 'chosen_samples_score': [0.9365938448744042], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8719, 'nll': 0.7540000946044921}, 'chosen_samples': ['11500'], 'chosen_samples_score': [1.0066637236145328], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8695, 'nll': 0.7271827411651611}, 'chosen_samples': ['28657'], 'chosen_samples_score': [0.9642316049774309], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8819, 'nll': 0.6653226722717285}, 'chosen_samples': ['22518'], 'chosen_samples_score': [1.0372975812969396], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8772, 'nll': 0.707529666519165}, 'chosen_samples': ['17501'], 'chosen_samples_score': [0.9744784427604912], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8882, 'nll': 0.6578324207305908}, 'chosen_samples': ['44350'], 'chosen_samples_score': [1.0363379478824946], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8913, 'nll': 0.6063134834289551}, 'chosen_samples': ['51986'], 'chosen_samples_score': [0.9764094493047342], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8893, 'nll': 0.7001789134979248}, 'chosen_samples': ['40084'], 'chosen_samples_score': [1.1410110638582076], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.893, 'nll': 0.6324515733718872}, 'chosen_samples': ['26733'], 'chosen_samples_score': [1.0226991711046636], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8952, 'nll': 0.6872901233673095}, 'chosen_samples': ['47613'], 'chosen_samples_score': [1.1484896652342387], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8966, 'nll': 0.6358540529251099}, 'chosen_samples': ['39344'], 'chosen_samples_score': [1.0419347952220661], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9001, 'nll': 0.635388258934021}, 'chosen_samples': ['45602'], 'chosen_samples_score': [1.0859394020766109], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.894, 'nll': 0.6707633726119995}, 'chosen_samples': ['20650'], 'chosen_samples_score': [1.0489212423472623], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9022, 'nll': 0.5998426966667175}, 'chosen_samples': ['22481'], 'chosen_samples_score': [1.1210811012760526], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.9009, 'nll': 0.5761639487266541}, 'chosen_samples': ['46126'], 'chosen_samples_score': [0.9575857183544196], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9029, 'nll': 0.6033371120452881}, 'chosen_samples': ['15779'], 'chosen_samples_score': [1.090745224972946], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8864, 'nll': 0.6333640552520752}, 'chosen_samples': ['43817'], 'chosen_samples_score': [0.9311849036872473], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9213, 'nll': 0.525291471004486}, 'chosen_samples': ['35401'], 'chosen_samples_score': [1.1558530701497718], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8995, 'nll': 0.6001166836738586}, 'chosen_samples': ['29132'], 'chosen_samples_score': [1.0517331713036904], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.8982, 'nll': 0.6302530660629272}, 'chosen_samples': ['13149'], 'chosen_samples_score': [1.0526062952449702], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9102, 'nll': 0.5736769578933716}, 'chosen_samples': ['14735'], 'chosen_samples_score': [1.1314708573479384], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9059, 'nll': 0.6189889925003051}, 'chosen_samples': ['1642'], 'chosen_samples_score': [1.2158435784802866], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9113, 'nll': 0.5796253247261047}, 'chosen_samples': ['14375'], 'chosen_samples_score': [1.1183595372290451], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9092, 'nll': 0.5607274585723877}, 'chosen_samples': ['5296'], 'chosen_samples_score': [1.1365478007055474], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9134, 'nll': 0.5240029829025269}, 'chosen_samples': ['26444'], 'chosen_samples_score': [1.0690290352582708], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9352, 'nll': 0.4695970127105713}, 'chosen_samples': ['4822'], 'chosen_samples_score': [1.1627154117496332], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9189, 'nll': 0.5440611932754517}, 'chosen_samples': ['5175'], 'chosen_samples_score': [1.0467936915539156], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9231, 'nll': 0.5247451298713685}, 'chosen_samples': ['12702'], 'chosen_samples_score': [1.105523947300263], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9187, 'nll': 0.5720467915534974}, 'chosen_samples': ['48649'], 'chosen_samples_score': [1.1514861822671496], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9223, 'nll': 0.5273438649177551}, 'chosen_samples': ['59390'], 'chosen_samples_score': [1.0400529732409767], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9298, 'nll': 0.49539837074279786}, 'chosen_samples': ['4360'], 'chosen_samples_score': [1.1572896390766498], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9302, 'nll': 0.4989057197570801}, 'chosen_samples': ['59759'], 'chosen_samples_score': [1.0053387023704219], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9361, 'nll': 0.47480933055877683}, 'chosen_samples': ['36515'], 'chosen_samples_score': [1.0961644016141392], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9367, 'nll': 0.49294519739151}, 'chosen_samples': ['31347'], 'chosen_samples_score': [1.2370261263233526], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9379, 'nll': 0.45449008350372316}, 'chosen_samples': ['7768'], 'chosen_samples_score': [1.13099343419798], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9366, 'nll': 0.4747372111320496}, 'chosen_samples': ['49282'], 'chosen_samples_score': [1.1278938247531602], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9304, 'nll': 0.46634747257232667}, 'chosen_samples': ['50274'], 'chosen_samples_score': [0.969442647375079], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9273, 'nll': 0.48173144607543944}, 'chosen_samples': ['34616'], 'chosen_samples_score': [1.077463746708455], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9356, 'nll': 0.4973913414955139}, 'chosen_samples': ['27596'], 'chosen_samples_score': [1.1702585523361666], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9375, 'nll': 0.5038139371871948}, 'chosen_samples': ['26300'], 'chosen_samples_score': [1.236029239028663], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9206, 'nll': 0.5368150390625}, 'chosen_samples': ['40654'], 'chosen_samples_score': [1.1127908473857833], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9374, 'nll': 0.4766683341026306}, 'chosen_samples': ['2426'], 'chosen_samples_score': [1.2151414869818957], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9306, 'nll': 0.48320088510513304}, 'chosen_samples': ['57523'], 'chosen_samples_score': [0.9959757996181788], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9327, 'nll': 0.4557098129272461}, 'chosen_samples': ['38133'], 'chosen_samples_score': [1.0898424755743061], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9378, 'nll': 0.465712580871582}, 'chosen_samples': ['16756'], 'chosen_samples_score': [1.0195743850810515], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9398, 'nll': 0.4661164938926697}, 'chosen_samples': ['8731'], 'chosen_samples_score': [1.1401598563527375], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9359, 'nll': 0.4553746457099915}, 'chosen_samples': ['517'], 'chosen_samples_score': [0.9792968267027417], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9382, 'nll': 0.4773662974357605}, 'chosen_samples': ['39778'], 'chosen_samples_score': [0.9870106543483823], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9405, 'nll': 0.43135197114944457}, 'chosen_samples': ['54'], 'chosen_samples_score': [1.0556060480129261], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9311, 'nll': 0.4838754554748535}, 'chosen_samples': ['39818'], 'chosen_samples_score': [0.9921153402676453], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9371, 'nll': 0.45910827341079713}, 'chosen_samples': ['38252'], 'chosen_samples_score': [1.0365028765236217], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9441, 'nll': 0.42943475379943846}, 'chosen_samples': ['1674'], 'chosen_samples_score': [1.1032223525862692], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9407, 'nll': 0.44064557571411134}, 'chosen_samples': ['29431'], 'chosen_samples_score': [1.0709427797913267], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9386, 'nll': 0.44689292449951173}, 'chosen_samples': ['20859'], 'chosen_samples_score': [1.162159182452251], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9371, 'nll': 0.43517749891281127}, 'chosen_samples': ['42428'], 'chosen_samples_score': [1.0833838968084617], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9441, 'nll': 0.43248333559036256}, 'chosen_samples': ['12768'], 'chosen_samples_score': [1.1315450697366827], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9427, 'nll': 0.4457635394096375}, 'chosen_samples': ['48752'], 'chosen_samples_score': [1.16480723508921], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9389, 'nll': 0.4466012600898743}, 'chosen_samples': ['52677'], 'chosen_samples_score': [1.1496805559638483], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9406, 'nll': 0.45152954845428467}, 'chosen_samples': ['24589'], 'chosen_samples_score': [1.0491438053411697], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9364, 'nll': 0.4665494839668274}, 'chosen_samples': ['11292'], 'chosen_samples_score': [1.0160363206186127], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9366, 'nll': 0.4541785945892334}, 'chosen_samples': ['9180'], 'chosen_samples_score': [1.2246182345811252], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.939, 'nll': 0.42089068775177}, 'chosen_samples': ['40589'], 'chosen_samples_score': [1.0442815925820184], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9406, 'nll': 0.4030318312644959}, 'chosen_samples': ['51180'], 'chosen_samples_score': [0.9992554196112671], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9405, 'nll': 0.42955201721191405}, 'chosen_samples': ['58470'], 'chosen_samples_score': [0.9494915277357118], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9397, 'nll': 0.4173881477355957}, 'chosen_samples': ['21023'], 'chosen_samples_score': [1.0231599213323972], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9336, 'nll': 0.4772615593910217}, 'chosen_samples': ['29791'], 'chosen_samples_score': [1.0929053217738607], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.934, 'nll': 0.47778180265426634}, 'chosen_samples': ['31301'], 'chosen_samples_score': [1.1509411264537164], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9412, 'nll': 0.41000413856506346}, 'chosen_samples': ['23733'], 'chosen_samples_score': [1.0721504096455217], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9313, 'nll': 0.49081918601989744}, 'chosen_samples': ['53872'], 'chosen_samples_score': [1.151042435703481], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9394, 'nll': 0.4231012782096863}, 'chosen_samples': ['53979'], 'chosen_samples_score': [0.9476582069996439], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9413, 'nll': 0.42436980695724486}, 'chosen_samples': ['2682'], 'chosen_samples_score': [1.076930768865415], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9483, 'nll': 0.4117795130729675}, 'chosen_samples': ['52140'], 'chosen_samples_score': [1.1197036675094547], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9404, 'nll': 0.42691101150512695}, 'chosen_samples': ['38061'], 'chosen_samples_score': [1.0161563910739493], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.94, 'nll': 0.43450945692062376}, 'chosen_samples': ['22531'], 'chosen_samples_score': [1.2638337964759718], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9412, 'nll': 0.40785509481430055}, 'chosen_samples': ['3742'], 'chosen_samples_score': [1.1041417461537717], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9452, 'nll': 0.42312674226760866}, 'chosen_samples': ['20869'], 'chosen_samples_score': [1.165253162612047], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9459, 'nll': 0.4067803901672363}, 'chosen_samples': ['22083'], 'chosen_samples_score': [0.9892683370258162], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9411, 'nll': 0.44051898651123045}, 'chosen_samples': ['21952'], 'chosen_samples_score': [1.1190219176274878], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9418, 'nll': 0.42565771551132203}, 'chosen_samples': ['43256'], 'chosen_samples_score': [1.1039064830847534], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9474, 'nll': 0.4134795603752136}, 'chosen_samples': ['20050'], 'chosen_samples_score': [1.0841008770236713], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9459, 'nll': 0.4346912770271301}, 'chosen_samples': ['15191'], 'chosen_samples_score': [1.024896480977948], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9492, 'nll': 0.3852515158653259}, 'chosen_samples': ['38389'], 'chosen_samples_score': [1.09443863480884], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9425, 'nll': 0.41321724863052367}, 'chosen_samples': ['50454'], 'chosen_samples_score': [0.9747996841429365], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9471, 'nll': 0.41298571615219115}, 'chosen_samples': ['49487'], 'chosen_samples_score': [1.0148709007795893], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9416, 'nll': 0.4324783709526062}, 'chosen_samples': ['46412'], 'chosen_samples_score': [1.182490321549642], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9392, 'nll': 0.4360050108909607}, 'chosen_samples': ['19590'], 'chosen_samples_score': [0.9585603130057923], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9402, 'nll': 0.42826672401428223}, 'chosen_samples': ['16572'], 'chosen_samples_score': [0.9504976463684401], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.943, 'nll': 0.4182699187278748}, 'chosen_samples': ['9340'], 'chosen_samples_score': [1.1269732389824965], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9435, 'nll': 0.4293200454711914}, 'chosen_samples': ['32776'], 'chosen_samples_score': [0.9854581814693645], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.946, 'nll': 0.3852525394439697}, 'chosen_samples': ['47513'], 'chosen_samples_score': [1.0945542456539648], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9459, 'nll': 0.3948049560546875}, 'chosen_samples': ['51144'], 'chosen_samples_score': [1.0548399198993632], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9437, 'nll': 0.42992434577941896}, 'chosen_samples': ['26358'], 'chosen_samples_score': [1.0289176295551248], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9464, 'nll': 0.40372001037597655}, 'chosen_samples': ['23086'], 'chosen_samples_score': [1.0782794073499753], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9397, 'nll': 0.4310448747634888}, 'chosen_samples': ['19942'], 'chosen_samples_score': [1.0541959613509075], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.951, 'nll': 0.4071546319007874}, 'chosen_samples': ['47511'], 'chosen_samples_score': [1.138032801526394], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.945, 'nll': 0.394613582611084}, 'chosen_samples': ['1075'], 'chosen_samples_score': [1.0077829864370513], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9455, 'nll': 0.4188998821258545}, 'chosen_samples': ['50097'], 'chosen_samples_score': [1.1159201551281335], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9501, 'nll': 0.3766430472373962}, 'chosen_samples': ['12305'], 'chosen_samples_score': [1.0060855602518102], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9513, 'nll': 0.39007480335235595}, 'chosen_samples': ['3370'], 'chosen_samples_score': [1.1533583652594988], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9498, 'nll': 0.373262189912796}, 'chosen_samples': ['13969'], 'chosen_samples_score': [1.1041917408140658], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9425, 'nll': 0.4120057830810547}, 'chosen_samples': ['59747'], 'chosen_samples_score': [0.9605900814046187], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9443, 'nll': 0.43299472885131834}, 'chosen_samples': ['59783'], 'chosen_samples_score': [0.9024769375348384], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9525, 'nll': 0.4187045156478882}, 'chosen_samples': ['57575'], 'chosen_samples_score': [1.2328518157010215], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9465, 'nll': 0.4285674118041992}, 'chosen_samples': ['34946'], 'chosen_samples_score': [0.9120561921163077], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9474, 'nll': 0.3729368958950043}, 'chosen_samples': ['670'], 'chosen_samples_score': [1.1020588412202845], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9431, 'nll': 0.40152977271080015}, 'chosen_samples': ['46709'], 'chosen_samples_score': [0.986180451214243], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9493, 'nll': 0.4015668667793274}, 'chosen_samples': ['56014'], 'chosen_samples_score': [1.1894645932865875], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.951, 'nll': 0.38024611587524415}, 'chosen_samples': ['36818'], 'chosen_samples_score': [1.156650787151646], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9536, 'nll': 0.3662747644901276}, 'chosen_samples': ['42209'], 'chosen_samples_score': [1.1558143176027462], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9536, 'nll': 0.38295628995895387}, 'chosen_samples': ['9221'], 'chosen_samples_score': [1.1444219898056758], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9557, 'nll': 0.37802403626441955}, 'chosen_samples': ['5679'], 'chosen_samples_score': [1.183212442645806], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9472, 'nll': 0.39255992078781127}, 'chosen_samples': ['51764'], 'chosen_samples_score': [0.9793142428868213], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9592, 'nll': 0.34570097885131834}, 'chosen_samples': ['13714'], 'chosen_samples_score': [1.10439238183735], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9535, 'nll': 0.360433318901062}, 'chosen_samples': ['27646'], 'chosen_samples_score': [1.073520710107705], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9513, 'nll': 0.3771974064826965}, 'chosen_samples': ['46088'], 'chosen_samples_score': [0.9989790170117402], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9561, 'nll': 0.34555452871322634}, 'chosen_samples': ['43796'], 'chosen_samples_score': [1.051101404109019], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9528, 'nll': 0.36526628437042236}, 'chosen_samples': ['13998'], 'chosen_samples_score': [0.9790304024302847], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9558, 'nll': 0.3576518928527832}, 'chosen_samples': ['47297'], 'chosen_samples_score': [1.1059576543234768], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9485, 'nll': 0.3898586621284485}, 'chosen_samples': ['30451'], 'chosen_samples_score': [1.1416401925008222], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9596, 'nll': 0.34799426441192627}, 'chosen_samples': ['18398'], 'chosen_samples_score': [1.060863822270056], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9553, 'nll': 0.3559809350013733}, 'chosen_samples': ['36984'], 'chosen_samples_score': [1.061772706386449], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9559, 'nll': 0.34032617530822756}, 'chosen_samples': ['13428'], 'chosen_samples_score': [1.0358530474531014], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9571, 'nll': 0.3577748791217804}, 'chosen_samples': ['39405'], 'chosen_samples_score': [1.1951961024375186], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9568, 'nll': 0.34527414064407347}, 'chosen_samples': ['55244'], 'chosen_samples_score': [1.055568527451232], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9509, 'nll': 0.3703533716678619}, 'chosen_samples': ['5842'], 'chosen_samples_score': [1.1008008996286103], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9555, 'nll': 0.3565607685089111}, 'chosen_samples': ['47983'], 'chosen_samples_score': [1.0557200065373755], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9557, 'nll': 0.34546964387893675}, 'chosen_samples': ['17958'], 'chosen_samples_score': [1.0481461200154127], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9578, 'nll': 0.3517537166595459}, 'chosen_samples': ['12986'], 'chosen_samples_score': [0.9610388582250834], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9573, 'nll': 0.36106368317604065}, 'chosen_samples': ['53062'], 'chosen_samples_score': [1.2142995941157668], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9574, 'nll': 0.36001718492507934}, 'chosen_samples': ['49517'], 'chosen_samples_score': [1.1876609756590386], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9559, 'nll': 0.36474723730087283}, 'chosen_samples': ['9552'], 'chosen_samples_score': [1.0125694955564448], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9615, 'nll': 0.3374520327568054}, 'chosen_samples': ['50912'], 'chosen_samples_score': [1.0362760777736222], 'chosen_samples_orignal_score': None})
